Morphometric image analysis of placental C-type lectin domain family 2, member D (CLEC2D) immuno-expression in HIV associated pre-eclampsia

C-type lectin 2D (CLEC2D) is upregulated in clear cell renal cell carcinoma (ccRCC) tissues and predicts poor prognosis

Clear cell renal cell carcinoma (ccRCC) is known as the most common type of renal cancer. Recently, a series of advances have been made in targeted therapy for ccRCC. To combat this highly metastatic tumor, novel therapeutic targets still need to be developed. C-type lectins (CLECs) contain a characteristic C-type lectin-like domain and affect several physiological functions. The effects of C-type lectin 2D (CLEC2D) on cancer progression have been revealed in several types of cancers; however, its expression in ccRCC tissues, and the possible effects on the progression and metastasis of ccRCC, are still unclear. Herein, we found the high mRNA and protein levels of CLEC2D in ccRCC tissues. We further found that CLEC2D expression was correlated with the prognosis of ccRCC patients and correlated with the tumor size (p = 0.019*) of patients. In addition, CLEC2D affected tumor immune infiltration, confirmed by the further analysis. CLEC2D knockdown suppressed the proliferation of ccRCC cells in vitro and restrained ccRCC tumor growth and immune infiltration in mice. Therefore, we believe that CLEC2D has the potential to serve as a promising ccRCC therapeutic target.

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.